Autonomous vehicles have become an active area of research over the past several decades. One of the biggest challenges for these vehicles is trajectory planning, in which a complex optimization problem must be solved in real-time, reliably, and typically on a system with limited computational resources. Many advances have been made in trajectory planning and optimization. Examples include nonlinear programming methods [1]}, [2]}, sampling based methods [3]}, [4]}, [5]}, and discrete motion planning such as A* [6]}, Hybrid A* [7]}, and Dijkstra's Algorithm [8]}. However, existing methods still suffer from drawbacks. Solving optimization problems in real-time can still be computationally challenging. In addition, sampling-based methods and discrete motion planners must be modified to account for kinodynamic constraints. This can be done by performing closed loop forward simulations (CLRRT) [5]} or using stereotyped behaviors such as motion primitives [10]}. Motion primitives (MPs) are an attractive option because they are a type of high-level action that does not need to be specified at each time step of the horizon. However, generating a motion primitive library can be time-consuming and non-intuitive.
